Overview

Dataset statistics

Number of variables13
Number of observations1119
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory113.8 KiB
Average record size in memory104.1 B

Variable types

Numeric12
Categorical1

Warnings

df_index has unique values Unique
citric acid has 89 (8.0%) zeros Zeros

Reproduction

Analysis started2021-03-25 13:02:19.065798
Analysis finished2021-03-25 13:02:36.724554
Duration17.66 seconds
Software versionpandas-profiling v2.11.0
Download configurationconfig.yaml

Variables

df_index
Real number (ℝ≥0)

UNIQUE

Distinct1119
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean803.5951743
Minimum0
Maximum1597
Zeros1
Zeros (%)0.1%
Memory size8.9 KiB
2021-03-25T10:02:36.796552image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile81.9
Q1402.5
median815
Q31211.5
95-th percentile1513.3
Maximum1597
Range1597
Interquartile range (IQR)809

Descriptive statistics

Standard deviation464.4960237
Coefficient of variation (CV)0.5780224154
Kurtosis-1.221779008
Mean803.5951743
Median Absolute Deviation (MAD)406
Skewness-0.02231380404
Sum899223
Variance215756.556
MonotocityNot monotonic
2021-03-25T10:02:36.894552image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
01
 
0.1%
10771
 
0.1%
10841
 
0.1%
10831
 
0.1%
10821
 
0.1%
10811
 
0.1%
10801
 
0.1%
10781
 
0.1%
10751
 
0.1%
10671
 
0.1%
Other values (1109)1109
99.1%
ValueCountFrequency (%)
01
0.1%
11
0.1%
21
0.1%
31
0.1%
61
0.1%
ValueCountFrequency (%)
15971
0.1%
15961
0.1%
15911
0.1%
15891
0.1%
15871
0.1%

fixed acidity
Real number (ℝ≥0)

Distinct92
Distinct (%)8.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.326988382
Minimum4.6
Maximum15.9
Zeros0
Zeros (%)0.0%
Memory size8.9 KiB
2021-03-25T10:02:37.161355image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum4.6
5-th percentile6.1
Q17.1
median7.9
Q39.3
95-th percentile11.7
Maximum15.9
Range11.3
Interquartile range (IQR)2.2

Descriptive statistics

Standard deviation1.751024477
Coefficient of variation (CV)0.2102830455
Kurtosis1.024266318
Mean8.326988382
Median Absolute Deviation (MAD)1
Skewness0.9546524359
Sum9317.9
Variance3.066086718
MonotocityNot monotonic
2021-03-25T10:02:37.259899image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7.243
 
3.8%
741
 
3.7%
7.141
 
3.7%
7.836
 
3.2%
7.736
 
3.2%
7.934
 
3.0%
7.333
 
2.9%
7.433
 
2.9%
8.333
 
2.9%
7.532
 
2.9%
Other values (82)757
67.6%
ValueCountFrequency (%)
4.61
 
0.1%
4.71
 
0.1%
4.91
 
0.1%
54
0.4%
5.12
0.2%
ValueCountFrequency (%)
15.91
0.1%
15.52
0.2%
152
0.2%
14.31
0.1%
13.81
0.1%

volatile acidity
Real number (ℝ≥0)

Distinct136
Distinct (%)12.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.5283735478
Minimum0.12
Maximum1.58
Zeros0
Zeros (%)0.0%
Memory size8.9 KiB
2021-03-25T10:02:37.365897image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0.12
5-th percentile0.26
Q10.39
median0.52
Q30.64
95-th percentile0.87
Maximum1.58
Range1.46
Interquartile range (IQR)0.25

Descriptive statistics

Standard deviation0.1872054391
Coefficient of variation (CV)0.3543050932
Kurtosis1.429278759
Mean0.5283735478
Median Absolute Deviation (MAD)0.125
Skewness0.734166583
Sum591.25
Variance0.03504587644
MonotocityNot monotonic
2021-03-25T10:02:37.460900image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.536
 
3.2%
0.632
 
2.9%
0.5929
 
2.6%
0.4328
 
2.5%
0.3927
 
2.4%
0.5626
 
2.3%
0.5425
 
2.2%
0.3625
 
2.2%
0.4924
 
2.1%
0.3424
 
2.1%
Other values (126)843
75.3%
ValueCountFrequency (%)
0.123
0.3%
0.161
 
0.1%
0.187
0.6%
0.192
 
0.2%
0.23
0.3%
ValueCountFrequency (%)
1.581
0.1%
1.332
0.2%
1.241
0.1%
1.1851
0.1%
1.181
0.1%

citric acid
Real number (ℝ≥0)

ZEROS

Distinct78
Distinct (%)7.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.2704647006
Minimum0
Maximum1
Zeros89
Zeros (%)8.0%
Memory size8.9 KiB
2021-03-25T10:02:37.569980image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.09
median0.26
Q30.42
95-th percentile0.6
Maximum1
Range1
Interquartile range (IQR)0.33

Descriptive statistics

Standard deviation0.1943724326
Coefficient of variation (CV)0.7186610012
Kurtosis-0.7481196384
Mean0.2704647006
Median Absolute Deviation (MAD)0.16
Skewness0.3227247484
Sum302.65
Variance0.03778064254
MonotocityNot monotonic
2021-03-25T10:02:37.673472image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
089
 
8.0%
0.4953
 
4.7%
0.0236
 
3.2%
0.2435
 
3.1%
0.128
 
2.5%
0.2628
 
2.5%
0.0126
 
2.3%
0.3224
 
2.1%
0.3123
 
2.1%
0.323
 
2.1%
Other values (68)754
67.4%
ValueCountFrequency (%)
089
8.0%
0.0126
 
2.3%
0.0236
3.2%
0.0320
 
1.8%
0.0419
 
1.7%
ValueCountFrequency (%)
11
 
0.1%
0.781
 
0.1%
0.761
 
0.1%
0.751
 
0.1%
0.744
0.4%

residual sugar
Real number (ℝ≥0)

Distinct82
Distinct (%)7.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.547140304
Minimum0.9
Maximum15.4
Zeros0
Zeros (%)0.0%
Memory size8.9 KiB
2021-03-25T10:02:37.773475image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0.9
5-th percentile1.5
Q11.9
median2.2
Q32.6
95-th percentile5.155
Maximum15.4
Range14.5
Interquartile range (IQR)0.7

Descriptive statistics

Standard deviation1.474163554
Coefficient of variation (CV)0.578752396
Kurtosis27.1701457
Mean2.547140304
Median Absolute Deviation (MAD)0.3
Skewness4.502597884
Sum2850.25
Variance2.173158183
MonotocityNot monotonic
2021-03-25T10:02:37.869475image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2114
 
10.2%
2.196
 
8.6%
1.890
 
8.0%
1.987
 
7.8%
2.286
 
7.7%
2.375
 
6.7%
2.559
 
5.3%
2.456
 
5.0%
1.754
 
4.8%
2.652
 
4.6%
Other values (72)350
31.3%
ValueCountFrequency (%)
0.91
 
0.1%
1.26
 
0.5%
1.34
 
0.4%
1.425
2.2%
1.524
2.1%
ValueCountFrequency (%)
15.42
0.2%
13.91
0.1%
13.82
0.2%
13.41
0.1%
12.91
0.1%

chlorides
Real number (ℝ≥0)

Distinct137
Distinct (%)12.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.08721179625
Minimum0.012
Maximum0.61
Zeros0
Zeros (%)0.0%
Memory size8.9 KiB
2021-03-25T10:02:37.976475image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0.012
5-th percentile0.0539
Q10.07
median0.079
Q30.09
95-th percentile0.1261
Maximum0.61
Range0.598
Interquartile range (IQR)0.02

Descriptive statistics

Standard deviation0.04635015679
Coefficient of variation (CV)0.5314665995
Kurtosis39.41137621
Mean0.08721179625
Median Absolute Deviation (MAD)0.01
Skewness5.522244191
Sum97.59
Variance0.002148337034
MonotocityNot monotonic
2021-03-25T10:02:38.077474image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0848
 
4.3%
0.07438
 
3.4%
0.08436
 
3.2%
0.07636
 
3.2%
0.07836
 
3.2%
0.07534
 
3.0%
0.07931
 
2.8%
0.08231
 
2.8%
0.07130
 
2.7%
0.07729
 
2.6%
Other values (127)770
68.8%
ValueCountFrequency (%)
0.0122
0.2%
0.0341
 
0.1%
0.0382
0.2%
0.0393
0.3%
0.0412
0.2%
ValueCountFrequency (%)
0.611
0.1%
0.4671
0.1%
0.4641
0.1%
0.4152
0.2%
0.4141
0.1%

free sulfur dioxide
Real number (ℝ≥0)

Distinct56
Distinct (%)5.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.9075067
Minimum1
Maximum72
Zeros0
Zeros (%)0.0%
Memory size8.9 KiB
2021-03-25T10:02:38.189475image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile4
Q17
median14
Q322
95-th percentile36
Maximum72
Range71
Interquartile range (IQR)15

Descriptive statistics

Standard deviation10.46171045
Coefficient of variation (CV)0.6576587168
Kurtosis1.287384821
Mean15.9075067
Median Absolute Deviation (MAD)7
Skewness1.121108554
Sum17800.5
Variance109.4473855
MonotocityNot monotonic
2021-03-25T10:02:38.285071image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6104
 
9.3%
572
 
6.4%
1061
 
5.5%
1551
 
4.6%
749
 
4.4%
1748
 
4.3%
1246
 
4.1%
843
 
3.8%
1643
 
3.8%
942
 
3.8%
Other values (46)560
50.0%
ValueCountFrequency (%)
12
 
0.2%
337
3.3%
426
 
2.3%
572
6.4%
5.51
 
0.1%
ValueCountFrequency (%)
721
0.1%
571
0.1%
552
0.2%
531
0.1%
521
0.1%

total sulfur dioxide
Real number (ℝ≥0)

Distinct137
Distinct (%)12.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean46.05317248
Minimum6
Maximum289
Zeros0
Zeros (%)0.0%
Memory size8.9 KiB
2021-03-25T10:02:38.389003image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum6
5-th percentile11
Q121.5
median37
Q361.5
95-th percentile111
Maximum289
Range283
Interquartile range (IQR)40

Descriptive statistics

Standard deviation32.72557722
Coefficient of variation (CV)0.7106041878
Kurtosis3.378574384
Mean46.05317248
Median Absolute Deviation (MAD)18
Skewness1.468079636
Sum51533.5
Variance1070.963405
MonotocityNot monotonic
2021-03-25T10:02:38.487184image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2832
 
2.9%
2429
 
2.6%
1426
 
2.3%
2325
 
2.2%
1323
 
2.1%
1223
 
2.1%
3123
 
2.1%
1823
 
2.1%
2022
 
2.0%
1922
 
2.0%
Other values (127)871
77.8%
ValueCountFrequency (%)
63
 
0.3%
73
 
0.3%
812
1.1%
99
0.8%
1021
1.9%
ValueCountFrequency (%)
2891
0.1%
1651
0.1%
1601
0.1%
1551
0.1%
1531
0.1%

density
Real number (ℝ≥0)

Distinct380
Distinct (%)34.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.9967307864
Minimum0.99007
Maximum1.00369
Zeros0
Zeros (%)0.0%
Memory size8.9 KiB
2021-03-25T10:02:38.593184image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0.99007
5-th percentile0.993694
Q10.9956
median0.99675
Q30.9978
95-th percentile0.99982
Maximum1.00369
Range0.01362
Interquartile range (IQR)0.0022

Descriptive statistics

Standard deviation0.001873231332
Coefficient of variation (CV)0.001879375412
Kurtosis0.9928372329
Mean0.9967307864
Median Absolute Deviation (MAD)0.00111
Skewness0.04475507833
Sum1115.34175
Variance3.508995624 × 106
MonotocityNot monotonic
2021-03-25T10:02:38.702185image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.997628
 
2.5%
0.997227
 
2.4%
0.996826
 
2.3%
0.99822
 
2.0%
0.996419
 
1.7%
0.996219
 
1.7%
0.997818
 
1.6%
0.999417
 
1.5%
0.998217
 
1.5%
0.997417
 
1.5%
Other values (370)909
81.2%
ValueCountFrequency (%)
0.990071
0.1%
0.99021
0.1%
0.990642
0.2%
0.990841
0.1%
0.99121
0.1%
ValueCountFrequency (%)
1.003692
0.2%
1.003152
0.2%
1.002891
0.1%
1.002422
0.2%
1.00221
0.1%

pH
Real number (ℝ≥0)

Distinct87
Distinct (%)7.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.311581769
Minimum2.74
Maximum4.01
Zeros0
Zeros (%)0.0%
Memory size8.9 KiB
2021-03-25T10:02:38.807184image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum2.74
5-th percentile3.07
Q13.2
median3.31
Q33.4
95-th percentile3.57
Maximum4.01
Range1.27
Interquartile range (IQR)0.2

Descriptive statistics

Standard deviation0.1554999447
Coefficient of variation (CV)0.04695639592
Kurtosis0.8283758425
Mean3.311581769
Median Absolute Deviation (MAD)0.1
Skewness0.2572464852
Sum3705.66
Variance0.0241802328
MonotocityNot monotonic
2021-03-25T10:02:38.914184image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3.3640
 
3.6%
3.338
 
3.4%
3.2637
 
3.3%
3.3835
 
3.1%
3.3935
 
3.1%
3.2932
 
2.9%
3.3431
 
2.8%
3.2830
 
2.7%
3.3529
 
2.6%
3.228
 
2.5%
Other values (77)784
70.1%
ValueCountFrequency (%)
2.741
0.1%
2.861
0.1%
2.871
0.1%
2.881
0.1%
2.892
0.2%
ValueCountFrequency (%)
4.011
0.1%
3.92
0.2%
3.851
0.1%
3.782
0.2%
3.751
0.1%

sulphates
Real number (ℝ≥0)

Distinct88
Distinct (%)7.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.6568007149
Minimum0.33
Maximum2
Zeros0
Zeros (%)0.0%
Memory size8.9 KiB
2021-03-25T10:02:39.022185image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0.33
5-th percentile0.47
Q10.55
median0.62
Q30.73
95-th percentile0.931
Maximum2
Range1.67
Interquartile range (IQR)0.18

Descriptive statistics

Standard deviation0.1717075835
Coefficient of variation (CV)0.2614302628
Kurtosis12.10349772
Mean0.6568007149
Median Absolute Deviation (MAD)0.08
Skewness2.465418694
Sum734.96
Variance0.02948349424
MonotocityNot monotonic
2021-03-25T10:02:39.129183image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.649
 
4.4%
0.6246
 
4.1%
0.5446
 
4.1%
0.5644
 
3.9%
0.5844
 
3.9%
0.5338
 
3.4%
0.6337
 
3.3%
0.5934
 
3.0%
0.5534
 
3.0%
0.6133
 
2.9%
Other values (78)714
63.8%
ValueCountFrequency (%)
0.331
 
0.1%
0.372
 
0.2%
0.396
0.5%
0.44
0.4%
0.422
 
0.2%
ValueCountFrequency (%)
21
0.1%
1.981
0.1%
1.951
0.1%
1.621
0.1%
1.611
0.1%

alcohol
Real number (ℝ≥0)

Distinct59
Distinct (%)5.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.42569258
Minimum8.4
Maximum14.9
Zeros0
Zeros (%)0.0%
Memory size8.9 KiB
2021-03-25T10:02:39.245183image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum8.4
5-th percentile9.2
Q19.5
median10.2
Q311.1
95-th percentile12.5
Maximum14.9
Range6.5
Interquartile range (IQR)1.6

Descriptive statistics

Standard deviation1.049734448
Coefficient of variation (CV)0.1006872627
Kurtosis0.2184899676
Mean10.42569258
Median Absolute Deviation (MAD)0.7
Skewness0.8466690132
Sum11666.35
Variance1.101942411
MonotocityNot monotonic
2021-03-25T10:02:39.347738image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
9.595
 
8.5%
9.476
 
6.8%
9.858
 
5.2%
9.251
 
4.6%
1048
 
4.3%
10.542
 
3.8%
9.641
 
3.7%
9.339
 
3.5%
1138
 
3.4%
9.738
 
3.4%
Other values (49)593
53.0%
ValueCountFrequency (%)
8.42
 
0.2%
8.51
 
0.1%
8.82
 
0.2%
919
1.7%
9.111
1.0%
ValueCountFrequency (%)
14.91
 
0.1%
144
0.4%
13.62
0.2%
13.51
 
0.1%
13.43
0.3%

quality
Categorical

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size8.9 KiB
0
967 
1
152 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1119
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row1
ValueCountFrequency (%)
0967
86.4%
1152
 
13.6%
2021-03-25T10:02:39.552692image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
2021-03-25T10:02:39.603694image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
0967
86.4%
1152
 
13.6%

Most occurring characters

ValueCountFrequency (%)
0967
86.4%
1152
 
13.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1119
100.0%

Most frequent character per category

ValueCountFrequency (%)
0967
86.4%
1152
 
13.6%

Most occurring scripts

ValueCountFrequency (%)
Common1119
100.0%

Most frequent character per script

ValueCountFrequency (%)
0967
86.4%
1152
 
13.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII1119
100.0%

Most frequent character per block

ValueCountFrequency (%)
0967
86.4%
1152
 
13.6%

Interactions

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Correlations

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Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2021-03-25T10:02:39.871456image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2021-03-25T10:02:40.070456image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2021-03-25T10:02:40.267456image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2021-03-25T10:02:36.388520image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
A simple visualization of nullity by column.
2021-03-25T10:02:36.613517image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

df_indexfixed acidityvolatile aciditycitric acidresidual sugarchloridesfree sulfur dioxidetotal sulfur dioxidedensitypHsulphatesalcoholquality
011697.60.500.292.30.0865.014.00.995023.320.6211.50
115106.40.360.212.20.04726.048.00.996613.470.779.70
29827.30.520.322.10.07051.070.00.994183.340.8212.90
315206.50.530.062.00.06329.044.00.994893.380.8310.30
42838.90.400.325.60.08710.047.00.999103.380.7710.51
58837.20.600.042.50.07618.088.00.997453.530.559.50
67609.00.580.252.80.0759.0104.00.997793.230.579.70
781210.80.450.332.50.09920.038.00.998183.240.7110.80
88226.70.540.132.00.07615.036.00.997303.610.649.80
913376.00.500.001.40.05715.026.00.994483.360.459.50

Last rows

df_indexfixed acidityvolatile aciditycitric acidresidual sugarchloridesfree sulfur dioxidetotal sulfur dioxidedensitypHsulphatesalcoholquality
110910946.60.7250.095.50.1179.017.00.996553.350.4910.80
11102248.40.6350.362.00.08915.055.00.997453.310.5710.40
111114287.80.6400.001.90.07227.055.00.996203.310.6311.00
1112698.00.7050.051.90.0748.019.00.996203.340.9510.50
11137588.10.8700.002.20.08410.031.00.996563.250.509.80
11149975.60.6600.002.20.0873.011.00.993783.710.6312.81
11159158.60.3150.402.20.0793.06.00.995123.270.6711.90
11166409.90.5400.452.30.07116.040.00.999103.390.629.40
111720612.80.3000.742.60.0959.028.00.999403.200.7710.81
111811857.00.4300.302.00.0856.039.00.993463.330.4611.90